An AI tool built for AI ECG heart disease detection identified 90% of heart valve disease cases and 81% of heart failure cases in a clinical trial of 67,000 patients in the US, researchers revealed at the European Society of Cardiology’s annual congress in Munich. The findings, reported by the Guardian, could change how patients are prioritised for follow-up scans in cardiology departments across the country.
Why the ECG Gap Has Mattered for So Long
The electrocardiogram has been a cornerstone of cardiac diagnostics for decades, measuring the heart’s electrical activity and helping clinicians assess risks from heart attacks and arrhythmia. Its limitation has always been clear: it cannot, on its own, detect heart disease. For that, patients need an echocardiogram, an ultrasound of the heart, and those appointments can be months away after referral.
Heart valve disease and heart failure are two of the most common forms of heart disease, and both can progress quietly. Without early detection, the first outwardly visible sign of heart failure can be cardiac arrest or sudden cardiac death. That diagnostic gap is precisely what this AI tool is designed to close.
According to Imperial BRC NIHR, AI-enhanced ECGs are already known to be highly accurate in diagnosing heart diseases, but had not previously been used to alert clinicians about findings in this way. This trial represents a shift in how that accuracy could be put to practical use in clinical settings, rather than remaining a research-stage capability.
AI ECG Heart Disease Detection: What the Trial Found
The model was trained on data from millions of patients before being tested against the 67,000 ECG readings. The results showed it could detect heart failure in 81% of cases and heart valve disease in 90% of cases. Those figures matter because the ECG is one of the most frequently ordered tests in medicine, with approximately a billion ECGs conducted every year worldwide. Plugging an AI detection layer into a test that already happens at that scale is a different proposition from building a new screening pathway from scratch.
Professor Fu Siong Ng, a professor of cardiology at Imperial College London who was involved in the study, highlighted the potential for patients who had ECGs ordered for entirely different reasons. ‘People may have undergone ECG tests for other reasons,’ he said, and the AI tool could incorporate heart disease checks into any list of diagnostic markers, ‘revealing potential heart disease when none was thought to exist.’ That opportunistic detection is one of the more practical arguments for the approach.
Professor Ng also pointed directly to the waiting-time problem. ‘Patients can often wait several months for a heart ultrasound scan after being referred for one by their doctor,’ he said. ‘This makes it exciting that our technology could identify patients most at risk of heart failure and heart valve disease, so they could be prioritised for scans faster and more urgently.’
Funded by the BHF, Cautious About What It Can Do
The trial was funded by the British Heart Foundation, and its clinical director was careful not to overstate what the tool delivers. Dr Sonya Babu-Narayan, a consultant cardiologist and clinical director of the BHF, said the AI ECG ‘will not detect everyone with a heart condition.’ Her framing was direct: ‘When it comes to the heart, earlier diagnosis and treatment saves and improves lives. Technology like the AI ECG in this research, which has the potential to identify high-risk patients early, will not detect everyone with a heart condition. But it could be a solution to help fast-track the patients who are most likely to have a heart abnormality.’
That caveat is worth holding alongside the headline detection rates. A flag from the AI tool would not confirm that a patient is about to have a heart attack. The envisaged pathway is that a flagged result moves someone up the queue for an echocardiogram, shortening the wait rather than replacing the scan.
Imperial College London, where Professor Ng is based, is among the institutions now considering how this kind of AI-enhanced ECG analysis could be integrated into cardiology departments. The next step is evaluating how the tool performs when deployed in real clinical workflows, where ECG volumes are high and the window for human review of each result is short.
